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Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction

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  • Tianxi Cai
  • Giulia Tonini
  • Xihong Lin

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  • Tianxi Cai & Giulia Tonini & Xihong Lin, 2011. "Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction," Biometrics, The International Biometric Society, vol. 67(3), pages 975-986, September.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:3:p:975-986
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2010.01544.x
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    References listed on IDEAS

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    1. Jelle J. Goeman & Sara A. Van De Geer & Hans C. Van Houwelingen, 2006. "Testing against a high dimensional alternative," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 477-493, June.
    2. Dawei Liu & Xihong Lin & Debashis Ghosh, 2007. "Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1079-1088, December.
    3. Yuhyun Park, 2003. "Estimating subject-specific survival functions under the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(3), pages 717-723, September.
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    Cited by:

    1. Lin Zhang & Inyoung Kim, 2021. "Finite mixtures of semiparametric Bayesian survival kernel machine regressions: Application to breast cancer gene pathway subgroup analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 251-269, March.
    2. Ghosh, Debashis, 2014. "An asymptotically minimax kernel machine," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 33-38.
    3. Yuanyuan Shen & Tianxi Cai, 2016. "Identifying predictive markers for personalized treatment selection," Biometrics, The International Biometric Society, vol. 72(4), pages 1017-1025, December.
    4. Sun Jiehuan & Herazo-Maya Jose D. & Kaminski Naftali & Wang Jane-Ling & Zhao Hongyu, 2019. "LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-9, April.
    5. Fan, Caiyun & Lu, Wenbin & Zhou, Yong, 2021. "Testing error heterogeneity in censored linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    6. Wenjing Qi & Andrew S Allen & Yi-Ju Li, 2019. "Family-based association tests for rare variants with censored traits," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-17, January.
    7. Cho, Youngjoo & Zhan, Xiang & Ghosh, Debashis, 2022. "Nonlinear predictive directions in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    8. Dehan Kong & Joseph G. Ibrahim & Eunjee Lee & Hongtu Zhu, 2018. "FLCRM: Functional linear cox regression model," Biometrics, The International Biometric Society, vol. 74(1), pages 109-117, March.

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